Knowledge base: The knowledge base is the main component of a knowledge based agent, also known as KB. This is a collection of sentences (here 'sentence' is a technical term and is not synonymous with sentences in English). These sentences are expressed in a language called knowledge representation language. The KBA knowledge base stores facts about the world.


An intelligent agent needs knowledge of the real world to make decisions and reasoning to act efficiently. Knowledge-based agents are agents who have the ability to maintain an internal state of knowledge, reason on that knowledge, update their knowledge after observations and take action. These agents can represent the world by formal representation and act intelligently.


Knowledge-based agents consist of two main parts, namely:
  - Knowledge base and
  - Inference system.


Knowledge-based agents must be able to do the following:
- The agent must be able to represent the circumstances, actions, etc.
- An agent must be able to incorporate new perceptions
- Agents can update the internal representation of the world
- An agent can infer the internal representation of the world
- Agent can conclude the appropriate action.


Inference means deriving new sentences from old ones. The inference system allows us to add new sentences to the knowledge base. A sentence is a proposition about the world. An inference system applies logical rules to KB to infer new information.


The inference system generates new facts so that agents can update the KB. The inference system works mainly in two rules which are given as:
- Front chain
- Reverse chain


Knowledge based agents can be seen at different levels which are given below:

1. Level of knowledge:
The knowledge level is the first level of a knowledge-based agent, and at this level, we need to determine what the agent knows, and what the agent's goals are. With those specifications, we can fix the behavior. For example, an auto taxi agent needs to get from station A to station B, and he knows the way from A to B, so it comes down to a level of knowledge.

2. Logical level:
At this level, we understand how knowledge representations of knowledge are stored. At this level, sentences are coded into a different logic. At the logical level, knowledge is encoded into logical sentences. At a logical level we can expect an automated taxi agent to reach destination B.

3. Implementation level:
It is a physical representation of logic and knowledge. At the implementation level, agents perform actions according to the level of logic and knowledge. At this level, an auto taxi agent really implements his knowledge and logic so that he can get to his destination.


The above material was delivered by a presenter from Ukraine in an international visiting lecturer held by STEKOM University in collaboration with Universities from Ukraine. The presenter's name is Oleksii Panasenko. He is a Lecturer at the Vinnitsia State Pedagogical University
(Ukraine), Ph.D. in Mathematics. He is also a data scientist at NestLogic Inc. His interest in science is in mathematics: fractal analysis. Meanwhile, his interest in teaching is working with mathematically gifted pupils; mathematical olympiads. In addition he has an interest in all things related to machine learning, data science, AI. The time for the visiting lecture to be held is on May 12 2023 with initial remarks by Dr. Joseph Teguh Santoso who is the Chancellor of STEKOM University and guided by Mrs. Novita.


This international webinar activity is part of the implementation of STEKOM University's commitment to increase various international activities. This was done in order to realize the vision to become an international-class university. Various international activities carried out by STEKOM University continue from year to year. There are international activities that are sustainable and there are also some international activities that are not sustainable. All types of international activities are accommodated and regulated by the International department of STEKOM University.